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**R Data Science:**
R Programming A-Z: R For Data Science With Real Exercises!

In logistic regression, we can select top variables based on their high wald chi-square value. In other words, we can run univariate analysis of each independent variable and then pick important predictors based on their wald chi-square value.

#Read Data File

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")

#Run Logistic Regression

mylogit <- glm(admit ~ ., data = mydata, family = "binomial")

#Create Logistic Regression Function

unilogit = function(df,depvar) {

depvar1 = deparse(substitute(depvar))

lapply(names(df)[which(names(df)!= depvar1)], function(x)

{mylogit = glm(formula(paste(depvar1,"~",x)), data = df, family = "binomial")

summary(mylogit)$coefficient}

)

}

#Run Function

univariate = unilogit(mydata, admit)

#Merge all the coefficients

final <- do.call(rbind, univariate)

#Make the table formatable

univList = cbind(data.frame(Variable = row.names(final)),final)

FinalList = subset(univList, Variable!="(Intercept)")

FinalList[,"Wald ChiSquare"] = FinalList[4]^2

FinalList[,"Rank"] = rank(-FinalList[6])

FinalList = FinalList[order(FinalList$Rank),]

**Method 2 :**unilogit2 = function(df,depvar, output) {

dummydt=data.frame(matrix(ncol=0,nrow=0))

depvar1 = deparse(substitute(depvar))

out = deparse(substitute(output))

xxxx = names(df)[which(names(df)!= depvar1)]

for (i in 1:length(xxxx)) {

mylogit = glm(formula(paste(depvar1,"~",xxxx[i])), data = df, family = "binomial")

coeff = data.frame(summary(mylogit)$coefficient)

if (i==1) {output = rbind(dummydt,coeff)}

else {output = rbind(output,coeff)}

assign(out,output, envir = .GlobalEnv)

}

}

unilogit2(mydata, admit, outtable)

is this for both continuous and categorical variables?

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